ProteoDisco: a flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies.

SUMMARY: We present an R-based open-source software termed ProteoDisco that allows for flexible incorporation of genomic variants, fusion genes and (aberrant) transcriptomic variants from standardized formats into protein variant sequences. ProteoDisco allows for a flexible step-by-step workflow allowing for in-depth customization to suit a myriad of research approaches in the field of proteogenomics, on all organisms for which a reference genome and transcript annotations are available. AVAILABILITY AND IMPLEMENTATION: ProteoDisco (R package version ≥ 1.0.0) is available on Bioconductor at https://doi.org/doi:10.18129/B9.bioc.ProteoDisco and from https://github.com/ErasmusMC-CCBC/ProteoDisco/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Compendium of AR Splice Variants in Metastatic Castration-Resistant Prostate Cancer.

Treatment-induced AR alterations, including AR alternative splice variants (AR-Vs), have been extensively linked to harboring roles in primary and acquired resistance to conventional and next-generation hormonal therapies in prostate cancer and therefore have gained momentum. Our aim was to uniformly determine recurrent AR-Vs in metastatic castration-resistant prostate cancer (mCRPC) using whole transcriptome sequencing in order to assess which AR-Vs might hold potential diagnostic or prognostic relevance in future research. This study reports that in addition to the promising AR-V7 as a biomarker, AR45 and AR-V3 were also seen as recurrent AR-Vs and that the presence of any AR-V could be associated with higher AR expression. With future research, these AR-Vs may therefore harbor similar or complementary roles to AR-V7 as predictive and prognostic biomarkers in mCRPC or as proxies for abundant AR expression.

The clonal relation of primary upper urinary tract urothelial carcinoma and paired urothelial carcinoma of the bladder.

The risk of developing urothelial carcinoma of the bladder (UCB) in patients treated by radical nephroureterectomy (RNU) for an upper urinary tract urothelial carcinoma (UTUC) is 22% to 47% in the 2 years after surgery. Subject of debate remains whether UTUC and the subsequent UCB are clonally related or represent separate origins. To investigate the clonal relationship between both entities, we performed targeted DNA sequencing of a panel of 41 genes on matched normal and tumor tissue of 15 primary UTUC patients treated by RNU who later developed 19 UCBs. Based on the detected tumor-specific DNA aberrations, the paired UTUC and UCB(s) of 11 patients (73.3%) showed a clonal relation, whereas in four patients the molecular results did not indicate a clear clonal relationship. Our results support the hypothesis that UCBs following a primary surgically resected UTUC are predominantly clonally derived recurrences and not separate entities.

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development.

BACKGROUND: Testicular germ cell cancer (TGCC), being the most frequent malignancy in young Caucasian males, is initiated from an embryonic germ cell. This study determines intratumour heterogeneity to unravel tumour progression from initiation until metastasis. METHODS: In total, 42 purified samples of four treatment-resistant nonseminomatous (NS) TGCC were investigated, including the precursor germ cell neoplasia in situ (GCNIS) and metastatic specimens, using whole-genome and targeted sequencing. Their evolution was reconstructed. RESULTS: Intratumour molecular heterogeneity did not correspond to the supposed primary tumour histological evolution. Metastases after systemic treatment could be derived from cancer stem cells not identified in the primary cancer. GCNIS mostly lacked the molecular marks of the primary NS and comprised dominant clones that failed to progress. A BRCA-like mutational signature was observed without evidence for direct involvement of BRCA1 and BRCA2 genes. CONCLUSIONS: Our data strongly support the hypothesis that NS is initiated by whole-genome duplication, followed by chromosome copy number alterations in the cancer stem cell population, and accumulation of low numbers of somatic mutations, even in therapy-resistant cases. These observations of heterogeneity at all stages of tumourigenesis should be considered when treating patients with GCNIS-only disease, or with clinically overt NS.

Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.

BACKGROUND: Current normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data. RESULTS: We observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality. CONCLUSIONS: We show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain.

Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning.

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden (q < 0.001), structural variants (q < 0.05), tandem duplications (q < 0.05) and deletions (q < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance.

Tobacco Smoking-Related Mutational Signatures in Classifying Smoking-Associated and Nonsmoking-Associated NSCLC.

INTRODUCTION: Patient-reported smoking history is frequently used as a stratification factor in NSCLC-directed clinical research. Nevertheless, this classification does not fully reflect the mutational processes in a tumor. Next-generation sequencing can identify mutational signatures associated with tobacco smoking, such as single-base signature 4 and indel-based signature 3. This provides an opportunity to redefine the classification of smoking- and nonsmoking-associated NSCLC on the basis of individual genomic tumor characteristics and could contribute to reducing the lung cancer stigma. METHODS: Whole genome sequencing data and clinical records were obtained from three prospective cohorts of metastatic NSCLC (N = 316). Relative contributions and absolute counts of single-base signature 4 and indel-based signature 3 were combined with relative contributions of age-related signatures to divide the cohort into smoking-associated ("smoking high") and nonsmoking-associated ("smoking low") clusters. RESULTS: The smoking high (n = 169) and smoking low (n = 147) clusters differed considerably in tumor mutational burden, signature contribution, and mutational landscape. This signature-based classification overlapped considerably with smoking history. Yet, 26% of patients with an active smoking history were included in the smoking low cluster, of which 52% harbored an EGFR/ALK/RET/ROS1 alteration, and 4% of patients without smoking history were included in the smoking high cluster. These discordant samples had similar genomic contexts to the rest of their respective cluster. CONCLUSIONS: A substantial subset of metastatic NSCLC is differently classified into smoking- and nonsmoking-associated tumors on the basis of smoking-related mutational signatures than on the basis of smoking history. This signature-based classification more accurately classifies patients on the basis of genome-wide context and should therefore be considered as a stratification factor in clinical research.

Identifying somatic changes in drug transporters using whole genome and transcriptome sequencing data of advanced tumors.

Drug resistance is a perpetual problem in cancer therapy with many underlying mechanisms. Alterations in drug transport over the cancer cell membrane can severely alter intratumoral drug exposure, contributing to resistance. Here, we present the somatic mutational landscape of 48 ATP-binding cassette and 416 solute carrier transporter genes in a cohort (CPCT-02; NCT01855477) of 3290 patients with different types of advanced and metastasized cancer through analysis of whole genome and transcriptome sequencing. In order to identify potential stressor mechanisms, we stratified patients based on previous systemic therapies and subsequently investigated the enrichment of mutations and copy-number alterations of transporter genes. In tumors from patients pretreated with protein kinase inhibitors (PKIs), genes encoding for specific copper (SLC31A1 and SLC31A2, χ2-test adjusted p-values: 6.9e-09 and 2.5e-09) and nucleoside transporters (SLC28A2 and SLC28A3, χ2-test adjusted p-values: 3.5e-06 and 6.8e-07) were deleted significantly more frequently than in patients pretreated with chemotherapy. Moreover, we detected 16 transporters that were differentially expressed at RNA level between these treatment groups. These findings contradict mechanisms of selective pressure, as they would be expected to originate during treatment with chemotherapy rather than with PKIs. Hence, they might constitute primary drug resistance mechanisms and, therefore, warrant further study.

mFast-SeqS-based aneuploidy score in circulating cell-free DNA is a prognostic biomarker in prostate cancer.

Multiple prognostic biomarkers, including circulating tumour cell (CTC) counts, exist in metastatic castration-resistant prostate cancer (mCRPC) patients, but none of them have been implemented into daily clinical care. The modified fast aneuploidy screening test-sequencing system (mFast-SeqS), which yields a genome-wide aneuploidy score, is able to reflect the fraction of cell-free tumour DNA (ctDNA) within cell-free DNA (cfDNA) and may be a promising biomarker in mCRPC. In this study, we investigated the prognostic value of dichotomized aneuploidy scores (< 5 vs. ≥ 5) as well as CTC counts (< 5 vs. ≥ 5) in 131 mCRPC patients prior to treatment with cabazitaxel. We validated our findings in an independent cohort of 50 similarly treated mCRPC patients. We observed that, similar to the dichotomized CTC count [HR: 2.92; 95% confidence interval (CI);1.84-4.62], dichotomized aneuploidy scores (HR: 3.24; CI: 2.12-4.94) significantly correlated with overall survival in mCRPC patients. We conclude that a dichotomized aneuploidy score from cfDNA is a prognostic marker for survival in mCRPC patients within our discovery cohort and in an independent mCRPC validation cohort. Therefore, this easy and robust minimally-invasive assay can be readily implemented as a prognostic marker in mCRPC. A dichotomized aneuploidy score might also be used as a stratification factor in clinical studies to account for tumour load.

Katdetectr: an R/bioconductor package utilizing unsupervised changepoint analysis for robust kataegis detection.

BACKGROUND: Kataegis refers to the occurrence of regional genomic hypermutation in cancer and is a phenomenon that has been observed in a wide range of malignancies. A kataegis locus constitutes a genomic region with a high mutation rate (i.e., a higher frequency of closely interspersed somatic variants than the overall mutational background). It has been shown that kataegis is of biological significance and possibly clinically relevant. Therefore, an accurate and robust workflow for kataegis detection is paramount. FINDINGS: Here we present Katdetectr, an open-source R/Bioconductor-based package for the robust yet flexible and fast detection of kataegis loci in genomic data. In addition, Katdetectr houses functionalities to characterize and visualize kataegis and provides results in a standardized format useful for subsequent analysis. In brief, Katdetectr imports industry-standard formats (MAF, VCF, and VRanges), determines the intermutation distance of the genomic variants, and performs unsupervised changepoint analysis utilizing the Pruned Exact Linear Time search algorithm followed by kataegis calling according to user-defined parameters.We used synthetic data and an a priori labeled pan-cancer dataset of whole-genome sequenced malignancies for the performance evaluation of Katdetectr and 5 publicly available kataegis detection packages. Our performance evaluation shows that Katdetectr is robust regarding tumor mutational burden and shows the fastest mean computation time. Additionally, Katdetectr reveals the highest accuracy (0.99, 0.99) and normalized Matthews correlation coefficient (0.98, 0.92) of all evaluated tools for both datasets. CONCLUSIONS: Katdetectr is a robust workflow for the detection, characterization, and visualization of kataegis and is available on Bioconductor: https://doi.org/doi:10.18129/B9.bioc.katdetectr.

Identification of Early-Onset Metastasis in SF3B1 Mutated Uveal Melanoma.

Approximately 25% of all uveal melanoma (UM) contain driver mutations in the gene encoding the spliceosome factor SF3B1, and whilst patients with such SF3B1 mutations generally have an intermediate risk on developing metastatic disease, a third of these patients develop early metastasis within 5 years after diagnosis. We therefore investigated whether clinical and/or genetic variables could be indicative of short progression-free survival (PFS < 60 months) or long PFS (PFS ≥ 60 months) for SF3B1-mutated (SF3B1mut) UM patients. We collected 146 SF3B1mut UM from our Rotterdam Ocular Melanoma Studygroup (ROMS) database and external published datasets. After stratification of all SF3B1mut UM using short PFS vs. long PFS, only largest tumor diameter (LTD) was significantly larger (mean: 17.7 mm (±2.8 SD) in the short PFS SF3B1mut group vs. the long PFS group (mean: 14.7 (±3.7 SD, p = 0.001). Combined ROMS and The Cancer Genome Atlas (TCGA) transcriptomic data were evaluated, and we identified SF3B1mut-specific canonical transcripts (e.g., a low expression of ABHD6 indicative for early-onset metastatic disease) or distinct expression of SF3B1mut UM aberrant transcripts, indicative of early- or late-onset or no metastatic SF3B1mut UM.

CRISPRs in the human genome are differentially expressed between malignant and normal adjacent to tumor tissue.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) have been identified in bacteria, archaea and mitochondria of plants, but not in eukaryotes. Here, we report the discovery of 12,572 putative CRISPRs randomly distributed across the human chromosomes, which we termed hCRISPRs. By using available transcriptome datasets, we demonstrate that hCRISPRs are distinctively expressed as small non-coding RNAs (sncRNAs) in cell lines and human tissues. Moreover, expression patterns thereof enabled us to distinguish normal from malignant tissues. In prostate cancer, we confirmed the differential hCRISPR expression between normal adjacent and malignant primary prostate tissue by RT-qPCR and demonstrate that the SHERLOCK and DETECTR dipstick tools are suitable to detect these sncRNAs. We anticipate that the discovery of CRISPRs in the human genome can be further exploited for diagnostic purposes in cancer and other medical conditions, which certainly will lead to the development of point-of-care tests based on the differential expression of the hCRISPRs.

Comprehensive Molecular Characterization Reveals Genomic and Transcriptomic Subtypes of Metastatic Urothelial Carcinoma.

Recent molecular characterization of primary urothelial carcinoma (UC) may guide future clinical decision-making. For metastatic UC (mUC), a comprehensive molecular characterization is still lacking. We analyzed whole-genome DNA and RNA sequencing data for fresh-frozen metastatic tumor biopsies from 116 mUC patients who were scheduled for palliative systemic treatment within the context of a clinical trial (NCT01855477 and NCT02925234). Hierarchical clustering for mutational signatures revealed two major genomic subtypes: GenS1 (67%), which was APOBEC-driven; and GenS2 (24%), which had a high fraction of de novo mutational signatures related to reactive oxygen species and is putatively clock-like. Significantly mutated genes (SMGs) did not differ between the genomic subtypes. Transcriptomic analysis revealed five mUC subtypes: luminal-a and luminal-b (40%), stroma-rich (24%), basal/squamous (23%), and a nonspecified subtype (12%). These subtypes differed regarding expression of key genes, SMGs, oncogenic pathway activity, and immune cell infiltration. We integrated the genomic and transcriptomic data to propose potential therapeutic options by transcriptomic subtype and for individual patients. This in-depth analysis of a large cohort of patients with mUC may serve as a reference for subtype-oriented and patient-specific research on the etiology of mUC and for novel drug development. PATIENT SUMMARY: We carried out an in-depth analysis of the molecular and genetic features of metastatic cancer involving the cells that line the urinary tract. We showed that this is a heterogeneous disease with different molecular subtypes and we identified possible targets for therapy for each subtype.

The EGFRvIII transcriptome in glioblastoma: A meta-omics analysis.

BACKGROUND: EGFR is among the genes most frequently altered in glioblastoma, with exons 2-7 deletions (EGFRvIII) being among its most common genomic mutations. There are conflicting reports about its prognostic role and it remains unclear whether and how it differs in signaling compared with wildtype EGFR. METHODS: To better understand the oncogenic role of EGFRvIII, we leveraged 4 large datasets into 1 large glioblastoma transcriptome dataset (n = 741) alongside 81 whole-genome samples from 2 datasets. RESULTS: The EGFRvIII/EGFR expression ratios differ strongly between tumors and range from 1% to 95%. Interestingly, the slope of relative EGFRvIII expression is near-linear, which argues against a more positive selection pressure than EGFR wildtype. An absence of selection pressure is also suggested by the similar survival between EGFRvIII-positive and -negative glioblastoma patients. EGFRvIII levels are inversely correlated with pan-EGFR (all wildtype and mutant variants) expression, which indicates that EGFRvIII has a higher potency in downstream pathway activation. EGFRvIII-positive glioblastomas have a lower CDK4 or MDM2 amplification incidence than EGFRvIII-negative (P = .007), which may point toward crosstalk between these pathways. EGFRvIII-expressing tumors have an upregulation of "classical" subtype genes compared to those with EGFR-amplification only (P = 3.873e-6). Genomic breakpoints of the EGFRvIII deletions have a preference toward the 3'-end of the large intron-1. These preferred breakpoints preserve a cryptic exon resulting in a novel EGFRvIII variant and preserve an intronic enhancer. CONCLUSIONS: These data provide deeper insights into the complex EGFRvIII biology and provide new insights for targeting EGFRvIII mutated tumors.

CABA-V7: a prospective biomarker selected trial of cabazitaxel treatment in AR-V7 positive prostate cancer patients.

BACKGROUND: Metastatic castration-resistant prostate cancer (mCRPC) patients with positive AR-V7 expression in their circulating tumour cells (CTCs) rarely derive benefit from abiraterone and enzalutamide. DESIGN: We performed a prospective, multicenter, single arm phase II clinical trial (CABA-V7) in mCRPC patients previously treated with docetaxel and androgen deprivation therapy. OBJECTIVE: In this trial, we investigated whether cabazitaxel treatment resulted in clinically meaningful PSA response rates in patients with positive CTC-based AR-V7 expression and collected liquid biopsies for genomic profiling. RESULTS: Cabazitaxel was found to be modestly effective, with only 12% of these patients obtaining a PSA response. Genomic profiling revealed that CTC-based AR-V7 expression was not associated with other known mCRPC-associated alterations. CTC-based AR-V7 status and dichotomised CTC counts were observed as independent prognostic markers at baseline. CONCLUSIONS: AR-V7 positivity predicted poor overall survival (OS). However, cabazitaxel-treated AR-V7 positive patients and those lacking AR-V7 positivity, who received cabazitaxel as standard of care, appeared to have similar OS. Therefore, despite the low response rate, cabazitaxel may still be an effective treatment in this poor prognosis, AR-V7 positive patient population.

11-Ketotestosterone is the predominant active androgen in prostate cancer patients after castration.

BACKGROUNDContinued androgen receptor (AR) signaling constitutes a key target for treatment in metastatic castration-resistant prostate cancer (CRPC). Studies have identified 11-ketotestosterone (11KT) as a potent AR agonist, but it is unknown if 11KT is present at physiologically relevant concentrations in patients with CRPC to drive AR activation. The goal of this study was to investigate the circulating steroid metabolome including all active androgens in patients with CRPC.METHODSPatients with metastatic CRPC (n = 29) starting a new line of systemic therapy were included. Sequential plasma samples were obtained for measurement of circulating steroid concentrations by multisteroid profiling employing liquid chromatography-tandem mass spectrometry. Metastatic tumor biopsy samples were obtained at baseline and subjected to RNA sequencing.RESULTS11KT was the most abundant circulating active androgen in 97% of patients with CRPC (median 0.39 nmol/L, range: 0.03-2.39 nmol/L), constituting 60% (IQR 43%-79%) of the total active androgen (TA) pool. Treatment with glucocorticoids reduced 11KT by 84% (49%-89%) and testosterone by 68% (38%-79%). Circulating TA concentrations at baseline were associated with a distinct intratumor gene expression signature comprising AR-regulated genes.CONCLUSIONThe potent AR agonist 11KT is the predominant circulating active androgen in patients with CRPC and, therefore, one of the potential drivers of AR activation in CRPC. Assessment of androgen status should be extended to include 11KT, as current clinical approaches likely underestimate androgen abundance in patients with CRPC.TRIAL REGISTRATIONNetherlands Trial Register: NL5625 (NTR5732).FUNDINGDaniel den Hoed Foundation and Wellcome Trust (Investigator Award WT209492/Z/17/Z).

Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features.

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance.

The genomic landscape of 85 advanced neuroendocrine neoplasms reveals subtype-heterogeneity and potential therapeutic targets.

Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) form clinically and genetically heterogeneous malignancies, characterized by distinct prognoses based upon primary tumor localization, functionality, grade, proliferation index and diverse outcomes to treatment. Here, we report the mutational landscape of 85 whole-genome sequenced aNEN. This landscape reveals distinct genomic subpopulations of aNEN based on primary localization and differentiation grade; we observe relatively high tumor mutational burdens (TMB) in neuroendocrine carcinoma (average 5.45 somatic mutations per megabase) with TP53, KRAS, RB1, CSMD3, APC, CSMD1, LRATD2, TRRAP and MYC as major drivers versus an overall low TMB in neuroendocrine tumors (1.09). Furthermore, we observe distinct drivers which are enriched in somatic aberrations in pancreatic (MEN1, ATRX, DAXX, DMD and CREBBP) and midgut-derived neuroendocrine tumors (CDKN1B). Finally, 49% of aNEN patients reveal potential therapeutic targets based upon actionable (and responsive) somatic aberrations within their genome; potentially directing improvements in aNEN treatment strategies.

Fusion transcripts and their genomic breakpoints in polyadenylated and ribosomal RNA-minus RNA sequencing data.

BACKGROUND: Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non-poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints. RESULTS: We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA-minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 TMPRSS2-ERG-positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq. CONCLUSION: By using the full potential of non-poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects.